A major goal for AI is to allow users to interact with agents that
learn in real time, making new kinds of interactive simulations,
training applications, and digital entertainment possible. This paper
describes such a learning technology, called real-time NeuroEvolution
of Augmenting Topologies (rtNEAT), and describes how rtNEAT was used
to build the NeuroEvolving Robotic Operatives (NERO) video game. This
game represents a new genre of machine learning games where the
player trains agents in real time to perform challenging tasks in a
virtual environment. Providing laymen the capability to effectively
train agents in real time with no prior knowledge of AI or machine
learning has broad implications, both in promoting the field of AI and
making its achievements accessible to the public at large.